Participatory Sensing for City-Scale Applications

2017 ◽  
pp. 210-230
Author(s):  
Tridib Mukherjee ◽  
Deepthi Chander ◽  
Sharanya Eswaran ◽  
Koustuv Dasgupta
Keyword(s):  
Smart Phones ◽  
Participatory Sensing ◽  
Community Based ◽  
Scientific Paradigm ◽  
Sensing Systems ◽  
Sensing Applications ◽  
Macro Scale ◽  
Urban Sensing

The rapid advancements in sensing, computation and communications have led to the proliferation of smart phones. People-centric sensing is a scientific paradigm which empowers citizens with sensor-embedded smartphones, to contribute to micro and macro-scale urban sensing applications – either implicitly (in an opportunistic manner) or explicitly (in a participatory manner). Community-based urban sensing applications, are typically participatory in nature. For instance, commuters reporting on a transit overload may explicitly need to provide an input through an app to report on the overload. This chapter will focus on the trends, challenges and applications of participatory sensing systems. Additionally, they will understand the solution requirements for effective deployments of such systems in real scenarios.

scholarly journals A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoguang Niu ◽  
Jiawei Wang ◽  
Qiongzan Ye ◽  
Yihao Zhang
Keyword(s):  
Dynamic Balance ◽  
Incentive Mechanism ◽  
Privacy Preserving ◽  
Circulation System ◽  
Participatory Sensing ◽  
Sensing Systems ◽  
Sensing Applications ◽  
Ideal Situation ◽  
Conflicting Objectives ◽  
Share Information

The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper introduces the notion of E-cent, an exchangeable unit bearer currency. Participants can use the E-cent to take part in tasks anonymously. By employing E-cent, we propose an E-cent-based privacy-preserving incentive mechanism, called EPPI. As a dynamic balance regulatory mechanism, EPPI can not only protect the privacy of participant, but also adjust the whole system to the ideal situation, under which the rated tasks can be finished at minimal cost. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

Mobile Crowd-sensing Applications: Data Redundancies, Challenges, and Solutions

2022 ◽  
Vol 22 (2) ◽  
pp. 1-15
Author(s):  
Tu N. Nguyen ◽  
Sherali Zeadally
Keyword(s):  
Mobile Devices ◽  
Cyber Physical System ◽  
Redundant Information ◽  
Mobile Crowd Sensing ◽  
Sensing System ◽  
Sensing Systems ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Mobile Crowd ◽  
Collection Methods

Conventional data collection methods that use Wireless Sensor Networks (WSNs) suffer from disadvantages such as deployment location limitation, geographical distance, as well as high construction and deployment costs of WSNs. Recently, various efforts have been promoting mobile crowd-sensing (such as a community with people using mobile devices) as a way to collect data based on existing resources. A Mobile Crowd-Sensing System can be considered as a Cyber-Physical System (CPS), because it allows people with mobile devices to collect and supply data to CPSs’ centers. In practical mobile crowd-sensing applications, due to limited budgets for the different expenditure categories in the system, it is necessary to minimize the collection of redundant information to save more resources for the investor. We study the problem of selecting participants in Mobile Crowd-Sensing Systems without redundant information such that the number of users is minimized and the number of records (events) reported by users is maximized, also known as the Participant-Report-Incident Redundant Avoidance (PRIRA) problem. We propose a new approximation algorithm, called the Maximum-Participant-Report Algorithm (MPRA) to solve the PRIRA problem. Through rigorous theoretical analysis and experimentation, we demonstrate that our proposed method performs well within reasonable bounds of computational complexity.

Incentives-based preferences and mobility-aware task assignment in participatory sensing systems

2018 ◽  
Vol 117 ◽  
pp. 71-83 ◽  
Author(s):  
Rim Ben Messaoud ◽  
Yacine Ghamri-Doudane ◽  
Dmitri Botvich
Keyword(s):  
Task Assignment ◽  
Participatory Sensing ◽  
Sensing Systems
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